AIMC Topic: Protein Engineering
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Machine learning-guided evolution of pyrrolysyl-tRNA synthetase for improved incorporation efficiency of diverse noncanonical amino acids.
Nature communications
Jul 19, 2025
The pyrrolysyl-tRNA synthetase (PylRS) is widely used to incorporate noncanonical amino acids (ncAAs) into proteins. However, the yields of most ncAA-containing protein remain low due to the limited activity of PylRS variants. Here, we apply machine...
Artificial intelligence-driven computational methods for antibody design and optimization.
mAbs
Jul 18, 2025
Antibodies play a crucial role in our immune system. Their ability to bind to and neutralize pathogens opens opportunities to develop antibodies for therapeutic and diagnostic use. Computational methods capable of designing antibodies for a target an...
Tuning antibody stability and function by rational designs of framework mutations.
mAbs
Jul 13, 2025
Artificial intelligence and machine learning models have been developed to engineer antibodies for specific recognition of antigens. These approaches, however, often focus on the antibody complementarity-determining region (CDR) whilst ignoring the i...
Modification and applications of glucose oxidase: optimization strategies and high-throughput screening technologies.
World journal of microbiology & biotechnology
Jul 12, 2025
Glucose oxidase (GOD), an oxidoreductase (EC 1.1.3.4), catalyzes the oxidation of β-D-glucose to gluconic acid using molecular oxygen as the electron acceptor, with concomitant generation of hydrogen peroxide. Owing to its versatile catalytic propert...
A generalized platform for artificial intelligence-powered autonomous enzyme engineering.
Nature communications
Jul 1, 2025
Proteins are the molecular machines of life with numerous applications in energy, health, and sustainability. However, engineering proteins with desired functions for practical applications remains slow, expensive, and specialist-dependent. Here we r...
Data-driven protease engineering by DNA-recording and epistasis-aware machine learning.
Nature communications
Jul 1, 2025
Protein engineering has recently seen tremendous transformation due to machine learning (ML) tools that predict structure from sequence at unprecedented precision. Predicting catalytic activity, however, remains challenging, restricting our capabilit...
Identification of Isomerically Diverse Ginsenosides Using Engineered Aerolysin Nanopore via Non-Translocation Blockade Sensing.
Angewandte Chemie (International ed. in English)
Jun 8, 2025
Typical nanopore sensing depends on slowed translocation through the pore to acquire effective blockade signals. However, this paradigm often suffers from low signal precision and poor resolution, making it challenging to resolve pools of analytes wi...
Designing diverse and high-performance proteins with a large language model in the loop.
PLoS computational biology
Jun 5, 2025
We present a protein engineering approach to directed evolution with machine learning that integrates a new semi-supervised neural network fitness prediction model, Seq2Fitness, and an innovative optimization algorithm, biphasic annealing for diverse...
EMOCPD: Efficient Attention-Based Models for Computational Protein Design Using Amino Acid Microenvironment.
Journal of chemical information and modeling
May 26, 2025
Computational protein design (CPD) refers to the use of computational methods to design proteins. Traditional methods relying on energy functions and heuristic algorithms for sequence design are inefficient and do not meet the demands of the big data...
Applying computational protein design to therapeutic antibody discovery - current state and perspectives.
Frontiers in immunology
May 22, 2025
Machine learning applications in protein sciences have ushered in a new era for designing molecules in silico. Antibodies, which currently form the largest group of biologics in clinical use, stand to benefit greatly from this shift. Despite the prol...
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